Next Article in Journal
Adaptive Model Predictive Control-Based Energy Management for Semi-Active Hybrid Energy Storage Systems on Electric Vehicles
Previous Article in Journal
Utilizing Non-Equilibrium Thermodynamics and Reactive Transport to Model CH4 Production from the Nankai Trough Gas Hydrate Reservoir
Article Menu
Issue 7 (July) cover image

Export Article

Open AccessArticle
Energies 2017, 10(7), 1069;

A Novel Workflow for Geothermal Prospectively Mapping Weights-of-Evidence in Liaoning Province, Northeast China

College of Earth Science, Jilin University, Changchun 130061, China
Xinjiang Uyghur Autonomous Region Academy of Surveying and Mapping, No. 231 Sandaowan East Road, Urumchi 830002, China
College of Information Sciences and Technology, Chengdu University of Technology, Chengdu 610059, China
Author to whom correspondence should be addressed.
Received: 3 July 2017 / Revised: 18 July 2017 / Accepted: 19 July 2017 / Published: 22 July 2017
(This article belongs to the Section Energy Sources)
Full-Text   |   PDF [7825 KB, uploaded 24 July 2017]   |  


Geological faults are highly developed in the eastern Liaoning Province in China, where Mesozoic granitic intrusions and Archean and Paleoproterozoic metamorphic rocks are widely distributed. Although the heat flow value in eastern Liaoning Province is generally low, the hot springs are very developed. It is obvious that the faults have significant control over the distribution of hot springs, and traditional methods of spatial data analysis such as WofE (weight of evidence) usually do not take into account the direction of the distribution of geothermal resources in the geothermal forecast process, which seriously affects the accuracy of the prediction results. To overcome the deficiency of the traditional evidence weight method, wherein it does not take the direction of evidence factor into account, this study put forward a combination of the Fry and WofE methods, Fry-WofE, based on geological observation, gravity, remote sensing, and DEM (digital elevation model) multivariate data. This study takes eastern Liaoning Province in China as an example, and the geothermal prospect was predicted respectively by the Fry-WofE and WofE methods from the statistical data on the spatial distribution of the exposed space of geothermal anomalies the surface. The result shows that the Fry-WofE method can achieve better prediction results when comparing the accuracy of these two methods. Based on the results of Fry-WofE prediction and water system extraction, 13 favorable geothermal prospect areas are delineated in eastern Liaoning Province. The Fry-WofE method is effective in study areas where the geothermal distribution area is obviously controlled by the fault. We provide not only a new method for solving the similar issue of geothermal exploration, but also a new insight into the distribution of geothermal resources in Liaoning Province. View Full-Text
Keywords: multi-information; fault; geothermal; 3S technology; weights-of-evidence; Fry-WofE multi-information; fault; geothermal; 3S technology; weights-of-evidence; Fry-WofE

Figure 1

This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited (CC BY 4.0).

Share & Cite This Article

MDPI and ACS Style

Sang, X.; Xue, L.; Liu, J.; Zhan, L. A Novel Workflow for Geothermal Prospectively Mapping Weights-of-Evidence in Liaoning Province, Northeast China. Energies 2017, 10, 1069.

Show more citation formats Show less citations formats

Note that from the first issue of 2016, MDPI journals use article numbers instead of page numbers. See further details here.

Related Articles

Article Metrics

Article Access Statistics



[Return to top]
Energies EISSN 1996-1073 Published by MDPI AG, Basel, Switzerland RSS E-Mail Table of Contents Alert
Back to Top